



'''simple docstring'''

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  '''scheduler_config.json'''

class 							a__( snake_case__     ):
			a_	: Optional[Any]									=      1
			a_	: List[str]									=      2
			a_	: Any									=      3
			a_	: int									=      4
			a_	: Tuple									=      5





@dataclass
class 							a__( snake_case__     ):
			a_	: jnp.ndarray





class 							a__:
			a_	: Any									=      SCHEDULER_CONFIG_NAME
			a_	: Any									=      ['''dtype''']
			a_	: Dict									=      []
			a_	: List[str]									=      True






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase=False					,   **_UpperCAmelCase					,   )							->							Dict:
					snake_case__     ,	snake_case__	     =cls.load_config(
					    pretrained_model_name_or_path=_UpperCAmelCase					,   subfolder=_UpperCAmelCase					,   return_unused_kwargs=_UpperCAmelCase					,   **_UpperCAmelCase					,   )
					snake_case__     ,	snake_case__	     =cls.from_config(_UpperCAmelCase					,   return_unused_kwargs=_UpperCAmelCase					,   **_UpperCAmelCase						)

					if hasattr(_UpperCAmelCase					,   'create_state'						) and getattr(_UpperCAmelCase					,   'has_state'					,   _UpperCAmelCase						):
							snake_case__	     =scheduler.create_state()

					if return_unused_kwargs:
							return scheduler, state, unused_kwargs

					return scheduler, state






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = False					,   **_UpperCAmelCase						)							->							Union[str, Any]:
					self.save_config(save_directory=_UpperCAmelCase					,   push_to_hub=_UpperCAmelCase					,   **_UpperCAmelCase						)






			@property
			def 						_lowercase  (     self						)							->							str:
					return self._get_compatibles()






			@classmethod
			def 						_lowercase  (     cls						)							->							str:
					snake_case__	     =list(set([cls.__name__] + cls._compatibles						)						)
					snake_case__	     =importlib.import_module(__name__.split('.'						)[0]						)
					snake_case__	     =[
					    getattr(_UpperCAmelCase					,   _UpperCAmelCase						) for c in compatible_classes_str if hasattr(_UpperCAmelCase					,   _UpperCAmelCase						)
					]
					return compatible_classes






def 				a  (     UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    Tuple[int]			)       ->	jnp.ndarray:
		assert len(UpperCamelCase_			) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase_			) - x.ndim)			)      ,					UpperCamelCase_			)






def 				a  (     UpperCamelCase_    :    int      ,					UpperCamelCase_    :    Union[str, Any]=0.9_9_9      ,					UpperCamelCase_    :    Union[str, Any]=jnp.floataa			)       ->	jnp.ndarray:

		def alpha_bar(UpperCamelCase_    :    Union[str, Any]			):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2			) ** 2

		snake_case__	     =[]
		for i in range(UpperCamelCase_			):
				snake_case__	     =i / num_diffusion_timesteps
				snake_case__	     =(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(UpperCamelCase_			) / alpha_bar(UpperCamelCase_			)      ,					UpperCamelCase_			)			)
		return jnp.array(UpperCamelCase_      ,					dtype=UpperCamelCase_			)

@flax.struct.dataclass
class 							a__:
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: jnp.ndarray






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     =scheduler.config

					if config.trained_betas is not None:
							snake_case__	     =jnp.asarray(config.trained_betas					,   dtype=scheduler.dtype						)
					elif config.beta_schedule == "linear":
							snake_case__	     =jnp.linspace(config.beta_start					,   config.beta_end					,   config.num_train_timesteps					,   dtype=scheduler.dtype						)
					elif config.beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							snake_case__	     =(
							    jnp.linspace(
							        config.beta_start**0.5					,   config.beta_end**0.5					,   config.num_train_timesteps					,   dtype=scheduler.dtype						)
							    ** 2
							)
					elif config.beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							snake_case__	     =betas_for_alpha_bar(config.num_train_timesteps					,   dtype=scheduler.dtype						)
					else:
							raise NotImplementedError(
							    f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""						)

					snake_case__	     =1.0 - betas

					snake_case__	     =jnp.cumprod(_UpperCAmelCase					,   axis=0						)

					return cls(
					    alphas=_UpperCAmelCase					,   betas=_UpperCAmelCase					,   alphas_cumprod=_UpperCAmelCase					,   )






def 				a  (     UpperCamelCase_    :    CommonSchedulerState      ,					UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    jnp.ndarray			)       ->	Optional[int]:
		snake_case__	     =state.alphas_cumprod

		snake_case__	     =alphas_cumprod[timesteps] ** 0.5
		snake_case__	     =sqrt_alpha_prod.flatten()
		snake_case__	     =broadcast_to_shape_from_left(UpperCamelCase_      ,					original_samples.shape			)

		snake_case__	     =(1 - alphas_cumprod[timesteps]) ** 0.5
		snake_case__	     =sqrt_one_minus_alpha_prod.flatten()
		snake_case__	     =broadcast_to_shape_from_left(UpperCamelCase_      ,					original_samples.shape			)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def 				a  (     UpperCamelCase_    :    CommonSchedulerState      ,					UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    jnp.ndarray			)       ->	Any:
		snake_case__     ,	snake_case__	     =get_sqrt_alpha_prod(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
		snake_case__	     =sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples






def 				a  (     UpperCamelCase_    :    CommonSchedulerState      ,					UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    jnp.ndarray			)       ->	Any:
		snake_case__     ,	snake_case__	     =get_sqrt_alpha_prod(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
		snake_case__	     =sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity









'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    list[str] | None = None      ,					UpperCamelCase_    :    dict[str, float] | None = None      ,					UpperCamelCase_    :    bool = False      ,					)       ->	tuple[int, float, str]:
		snake_case__	     =cipher_alphabet or [chr(UpperCamelCase_			) for i in range(97      ,					123			)]

		# If the argument is None or the user provided an empty dictionary
		if not frequencies_dict:
				# Frequencies of letters in the english language (how much they show up)
				snake_case__	     ={
				    'a': 0.0_8_4_9_7,
				    'b': 0.0_1_4_9_2,
				    'c': 0.0_2_2_0_2,
				    'd': 0.0_4_2_5_3,
				    'e': 0.1_1_1_6_2,
				    'f': 0.0_2_2_2_8,
				    'g': 0.0_2_0_1_5,
				    'h': 0.0_6_0_9_4,
				    'i': 0.0_7_5_4_6,
				    'j': 0.0_0_1_5_3,
				    'k': 0.0_1_2_9_2,
				    'l': 0.0_4_0_2_5,
				    'm': 0.0_2_4_0_6,
				    'n': 0.0_6_7_4_9,
				    'o': 0.0_7_5_0_7,
				    'p': 0.0_1_9_2_9,
				    'q': 0.0_0_0_9_5,
				    'r': 0.0_7_5_8_7,
				    's': 0.0_6_3_2_7,
				    't': 0.0_9_3_5_6,
				    'u': 0.0_2_7_5_8,
				    'v': 0.0_0_9_7_8,
				    'w': 0.0_2_5_6_0,
				    'x': 0.0_0_1_5_0,
				    'y': 0.0_1_9_9_4,
				    'z': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				snake_case__	     =frequencies_dict

		if not case_sensitive:
				snake_case__	     =ciphertext.lower()

		# Chi squared statistic values
		snake_case__	     ={}

		# cycle through all of the shifts
		for shift in range(len(UpperCamelCase_			)			):
				snake_case__	     =''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								snake_case__	     =(alphabet_letters.index(letter.lower()			) - shift) % len(
								    UpperCamelCase_			)
								decrypted_with_shift += (
								    alphabet_letters[new_key].upper()
								    if case_sensitive and letter.isupper()
								    else alphabet_letters[new_key]
								)
						except ValueError:
								# Append the character if it isn't in the alphabet
								decrypted_with_shift += letter

				snake_case__	     =0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								snake_case__	     =letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.lower().count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value
						else:
								if letter.lower() in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
				snake_case__	     =(
				    chi_squared_statistic,
				    decrypted_with_shift,
				)

		# Get the most likely cipher by finding the cipher with the smallest chi squared
		# statistic
		def chi_squared_statistic_values_sorting_key(UpperCamelCase_    :    int			) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		snake_case__	     =min(
		    UpperCamelCase_      ,					key=UpperCamelCase_      ,					)

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    snake_case__
		)     ,	(
		    snake_case__
		)     ,	
		)	     =chi_squared_statistic_values[most_likely_cipher]

		# Return the data on the most likely shift
		return (
		    most_likely_cipher,
		    most_likely_cipher_chi_squared_value,
		    decoded_most_likely_cipher,
		)




